An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea
This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examin...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier Ltd
2011
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| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0307904X10002295 http://hdl.handle.net/20.500.11937/49554 |
| _version_ | 1848758264447107072 |
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| author | Azadeh, A. Saberi, Morteza Asadzadeh, S. |
| author_facet | Azadeh, A. Saberi, Morteza Asadzadeh, S. |
| author_sort | Azadeh, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries. |
| first_indexed | 2025-11-14T09:41:13Z |
| format | Journal Article |
| id | curtin-20.500.11937-49554 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:41:13Z |
| publishDate | 2011 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-495542017-03-15T22:55:35Z An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea Azadeh, A. Saberi, Morteza Asadzadeh, S. Post processing Oil consumption estimation Auto regression (AR) Simulation Analysis of variance (ANOVA) Adaptive network based fuzzy inference system (ANFIS) This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries. 2011 Journal Article http://hdl.handle.net/20.500.11937/49554 http://www.sciencedirect.com/science/article/pii/S0307904X10002295 Elsevier Ltd restricted |
| spellingShingle | Post processing Oil consumption estimation Auto regression (AR) Simulation Analysis of variance (ANOVA) Adaptive network based fuzzy inference system (ANFIS) Azadeh, A. Saberi, Morteza Asadzadeh, S. An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea |
| title | An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea |
| title_full | An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea |
| title_fullStr | An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea |
| title_full_unstemmed | An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea |
| title_short | An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea |
| title_sort | adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: the cases of canada, united kingdom, and south korea |
| topic | Post processing Oil consumption estimation Auto regression (AR) Simulation Analysis of variance (ANOVA) Adaptive network based fuzzy inference system (ANFIS) |
| url | http://www.sciencedirect.com/science/article/pii/S0307904X10002295 http://hdl.handle.net/20.500.11937/49554 |